






'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







  @property
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			1
         a_						     =			3
         a_						     =			(32, 32)

         a_						     =			floats_tensor((batch_size, num_channels) + sizes   ,	rng=random.Random(0	)	).to(UpperCamelCase__	)
         return image







  @property
  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)
         a_						     =			UNetaDConditionModel(
             block_out_channels=(32, 64)   ,	layers_per_block=2   ,	sample_size=32   ,	in_channels=4   ,	out_channels=4   ,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,	cross_attention_dim=32   ,	)
         return model







  @property
  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)
         a_						     =			AutoencoderKL(
             block_out_channels=[32, 64]   ,	in_channels=3   ,	out_channels=3   ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,	latent_channels=4   ,	)
         return model







  @property
  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)
         a_						     =			RobertaSeriesConfig(
             hidden_size=32   ,	project_dim=32   ,	intermediate_size=37   ,	layer_norm_eps=1e-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=5_006   ,	)
         return RobertaSeriesModelWithTransformation(UpperCamelCase__	)







  @property
  def    _a       (			self	):






         """simple docstring"""


         def extract(*UpperCamelCase__   ,	**UpperCamelCase__	):




                class   __SCREAMING_SNAKE_CASE       :


                  """simple docstring"""


                  def __init__(			self	):






                         """simple docstring"""


                         a_						     =			torch.ones([0]	)







                  def    _a       (			self   ,	UpperCamelCase__	):






                         """simple docstring"""


                         self.pixel_values.to(UpperCamelCase__	)
                         return self





                return Out()

         return extract







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			'cpu'  # ensure determinism for the device-dependent torch.Generator
         a_						     =			self.dummy_cond_unet
         a_						     =			PNDMScheduler(skip_prk_steps=UpperCamelCase__	)
         a_						     =			self.dummy_vae
         a_						     =			self.dummy_text_encoder
         a_						     =			XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'	)
         a_						     =			77

         a_						     =			self.dummy_image.to(UpperCamelCase__	)
         a_						     =			init_image / 2 + 0.5

         # make sure here that pndm scheduler skips prk
         a_						     =			AltDiffusionImgaImgPipeline(
             unet=UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	vae=UpperCamelCase__   ,	text_encoder=UpperCamelCase__   ,	tokenizer=UpperCamelCase__   ,	safety_checker=UpperCamelCase__   ,	feature_extractor=self.dummy_extractor   ,	)
         a_						     =			VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor   ,	do_normalize=UpperCamelCase__	)
         a_						     =			alt_pipe.to(UpperCamelCase__	)
         alt_pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			'A painting of a squirrel eating a burger'
         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			alt_pipe(
             [prompt]   ,	generator=UpperCamelCase__   ,	guidance_scale=6.0   ,	num_inference_steps=2   ,	output_type='np'   ,	image=UpperCamelCase__   ,	)

         a_						     =			output.images

         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			alt_pipe(
             [prompt]   ,	generator=UpperCamelCase__   ,	guidance_scale=6.0   ,	num_inference_steps=2   ,	output_type='np'   ,	image=UpperCamelCase__   ,	return_dict=UpperCamelCase__   ,	)[0]

         a_						     =			image[0, -3:, -3:, -1]
         a_						     =			image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         a_						     =			np.array([0.4_427, 0.3_731, 0.4_249, 0.4_941, 0.4_546, 0.4_148, 0.4_193, 0.4_666, 0.4_499]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 5e-3
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 5e-3







  @unittest.skipIf(torch_device != 'cuda'   ,	'This test requires a GPU'	)
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.dummy_cond_unet
         a_						     =			PNDMScheduler(skip_prk_steps=UpperCamelCase__	)
         a_						     =			self.dummy_vae
         a_						     =			self.dummy_text_encoder
         a_						     =			XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'	)
         a_						     =			77

         a_						     =			self.dummy_image.to(UpperCamelCase__	)

         # put models in fp16
         a_						     =			unet.half()
         a_						     =			vae.half()
         a_						     =			bert.half()

         # make sure here that pndm scheduler skips prk
         a_						     =			AltDiffusionImgaImgPipeline(
             unet=UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	vae=UpperCamelCase__   ,	text_encoder=UpperCamelCase__   ,	tokenizer=UpperCamelCase__   ,	safety_checker=UpperCamelCase__   ,	feature_extractor=self.dummy_extractor   ,	)
         a_						     =			VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor   ,	do_normalize=UpperCamelCase__	)
         a_						     =			alt_pipe.to(UpperCamelCase__	)
         alt_pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			'A painting of a squirrel eating a burger'
         a_						     =			torch.manual_seed(0	)
         a_						     =			alt_pipe(
             [prompt]   ,	generator=UpperCamelCase__   ,	num_inference_steps=2   ,	output_type='np'   ,	image=UpperCamelCase__   ,	).images

         assert image.shape == (1, 32, 32, 3)







  @unittest.skipIf(torch_device != 'cuda'   ,	'This test requires a GPU'	)
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/img2img/sketch-mountains-input.jpg'	)
         # resize to resolution that is divisible by 8 but not 16 or 32
         a_						     =			init_image.resize((760, 504)	)

         a_						     =			'BAAI/AltDiffusion'
         a_						     =			AltDiffusionImgaImgPipeline.from_pretrained(
             UpperCamelCase__   ,	safety_checker=UpperCamelCase__   ,	)
         pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)
         pipe.enable_attention_slicing()

         a_						     =			'A fantasy landscape, trending on artstation'

         a_						     =			torch.manual_seed(0	)
         a_						     =			pipe(
             prompt=UpperCamelCase__   ,	image=UpperCamelCase__   ,	strength=0.75   ,	guidance_scale=7.5   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	)
         a_						     =			output.images[0]

         a_						     =			image[255:258, 383:386, -1]

         assert image.shape == (504, 760, 3)
         a_						     =			np.array([0.9_358, 0.9_397, 0.9_599, 0.9_901, 1.0_000, 1.0_000, 0.9_882, 1.0_000, 1.0_000]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2





@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/img2img/sketch-mountains-input.jpg'	)
         a_						     =			init_image.resize((768, 512)	)
         a_						     =			load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'	)

         a_						     =			'BAAI/AltDiffusion'
         a_						     =			AltDiffusionImgaImgPipeline.from_pretrained(
             UpperCamelCase__   ,	safety_checker=UpperCamelCase__   ,	)
         pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)
         pipe.enable_attention_slicing()

         a_						     =			'A fantasy landscape, trending on artstation'

         a_						     =			torch.manual_seed(0	)
         a_						     =			pipe(
             prompt=UpperCamelCase__   ,	image=UpperCamelCase__   ,	strength=0.75   ,	guidance_scale=7.5   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	)
         a_						     =			output.images[0]

         assert image.shape == (512, 768, 3)
         # img2img is flaky across GPUs even in fp32, so using MAE here
         assert np.abs(expected_image - image	).max() < 1e-2












'''simple docstring'''







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


__lowerCAmelCase        =							False




class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







  @property
  def    _a       (			self	):






         """simple docstring"""


         return 12







  @property
  def    _a       (			self	):






         """simple docstring"""


         return 12







  @property
  def    _a       (			self	):






         """simple docstring"""


         return 32







  @property
  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)
         a_						     =			VQModel(
             block_out_channels=[32, 64]   ,	in_channels=3   ,	out_channels=3   ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,	latent_channels=3   ,	num_vq_embeddings=self.num_embed   ,	vq_embed_dim=3   ,	)
         return model







  @property
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)
         return tokenizer







  @property
  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)
         a_						     =			CLIPTextConfig(
             bos_token_id=0   ,	eos_token_id=2   ,	hidden_size=self.text_embedder_hidden_size   ,	intermediate_size=37   ,	layer_norm_eps=1e-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=1_000   ,	)
         return CLIPTextModel(UpperCamelCase__	)







  @property
  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)

         a_						     =			12
         a_						     =			12

         a_						     =			{
             'attention_bias': True,
             'cross_attention_dim': 32,
             'attention_head_dim': height * width,
             'num_attention_heads': 1,
             'num_vector_embeds': self.num_embed,
             'num_embeds_ada_norm': self.num_embeds_ada_norm,
             'norm_num_groups': 32,
             'sample_size': width,
             'activation_fn': 'geglu-approximate',
         }

         a_						     =			TransformeraDModel(**UpperCamelCase__	)
         return model







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			'cpu'

         a_						     =			self.dummy_vqvae
         a_						     =			self.dummy_text_encoder
         a_						     =			self.dummy_tokenizer
         a_						     =			self.dummy_transformer
         a_						     =			VQDiffusionScheduler(self.num_embed	)
         a_						     =			LearnedClassifierFreeSamplingEmbeddings(learnable=UpperCamelCase__	)

         a_						     =			VQDiffusionPipeline(
             vqvae=UpperCamelCase__   ,	text_encoder=UpperCamelCase__   ,	tokenizer=UpperCamelCase__   ,	transformer=UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	learned_classifier_free_sampling_embeddings=UpperCamelCase__   ,	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			'teddy bear playing in the pool'

         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			pipe([prompt]   ,	generator=UpperCamelCase__   ,	num_inference_steps=2   ,	output_type='np'	)
         a_						     =			output.images

         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			pipe(
             [prompt]   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	return_dict=UpperCamelCase__   ,	num_inference_steps=2	)[0]

         a_						     =			image[0, -3:, -3:, -1]
         a_						     =			image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 24, 24, 3)

         a_						     =			np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			'cpu'

         a_						     =			self.dummy_vqvae
         a_						     =			self.dummy_text_encoder
         a_						     =			self.dummy_tokenizer
         a_						     =			self.dummy_transformer
         a_						     =			VQDiffusionScheduler(self.num_embed	)
         a_						     =			LearnedClassifierFreeSamplingEmbeddings(
             learnable=UpperCamelCase__   ,	hidden_size=self.text_embedder_hidden_size   ,	length=tokenizer.model_max_length	)

         a_						     =			VQDiffusionPipeline(
             vqvae=UpperCamelCase__   ,	text_encoder=UpperCamelCase__   ,	tokenizer=UpperCamelCase__   ,	transformer=UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	learned_classifier_free_sampling_embeddings=UpperCamelCase__   ,	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			'teddy bear playing in the pool'

         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			pipe([prompt]   ,	generator=UpperCamelCase__   ,	num_inference_steps=2   ,	output_type='np'	)
         a_						     =			output.images

         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			pipe(
             [prompt]   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	return_dict=UpperCamelCase__   ,	num_inference_steps=2	)[0]

         a_						     =			image[0, -3:, -3:, -1]
         a_						     =			image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 24, 24, 3)

         a_						     =			np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 2.0
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2





@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'	)

         a_						     =			VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq'	)
         a_						     =			pipeline.to(UpperCamelCase__	)
         pipeline.set_progress_bar_config(disable=UpperCamelCase__	)

         # requires GPU generator for gumbel softmax
         # don't use GPU generator in tests though
         a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(0	)
         a_						     =			pipeline(
             'teddy bear playing in the pool'   ,	num_images_per_prompt=1   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	)

         a_						     =			output.images[0]

         assert image.shape == (256, 256, 3)
         assert np.abs(expected_image - image	).max() < 2.0




